Intra-tumoural lipid composition and lymphovascular invasion in breast cancer via non-invasive magnetic resonance spectroscopy

Cheung, Sai Man; Husain, Ehab; Mallikourti, Vasiliki; Masannat, Yazan; Heys, Steven Darryll; He, Jiabao

doi:10.1007/s00330-020-07502-4

Cited by 21 publications

(13 citation statements)

References 43 publications

(75 reference statements)

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“…Except for lactic acid, carbonic anhydrase 9 (CA-9), maintaining the acidic condition of TME, also contributed to the tumor progression ( 38 ). Interestingly, the lipid composition in TME is also an important factor for macrophages recruitment and functional remolding ( 39 ). In our study, by using the co-culturing system in vitro , we find that the recruitment of macrophages is strongly enhanced by LDHA.…”

Section: Discussionmentioning

confidence: 99%

Lactate Dehydrogenase-A (LDH-A) Preserves Cancer Stemness and Recruitment of Tumor-Associated Macrophages to Promote Breast Cancer Progression

Wang

et al. 2021

Front. Oncol.

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Increasing evidence reveals that breast cancer stem cells (BCSCs) subtypes with distinct properties are regulated by their abnormal metabolic changes; however, the specific molecular mechanism and its relationship with tumor microenvironment (TME) are not clear. In this study, we explored the mechanism of lactate dehydrogenase A (LDHA), a crucial glycolytic enzyme, in maintaining cancer stemness and BCSCs plasticity, and promoting the interaction of BCSCs with tumor associated macrophages (TAMs). Firstly, the expression of LDHA in breast cancer tissues was much higher than that in adjacent tissues and correlated with the clinical progression and prognosis of breast cancer patients based on The Cancer Genome Atlas (TCGA) data set. Moreover, the orthotopic tumor growth and pulmonary metastasis were remarkable inhibited in mice inoculated with 4T1-shLdha cells. Secondly, the properties of cancer stemness were significantly suppressed in MDA-MB-231-shLDHA or A549-shLDHA cancer cells, including the decrease of ALDH+ cells proportion, the repression of sphere formation and cellular migration, and the reduction of stemness genes (SOX2, OCT4, and NANOG) expression. However, the proportion of ALDH+ cells (epithelial-like BCSCs, E-BCSCs) was increased and the proportion of CD44+ CD24− cells (mesenchyme-like BCSCs, M-BCSCs) was decreased after LDHA silencing, suggesting a regulatory role of LDHA in E-BCSCs/M-BCSCs transformation in mouse breast cancer cells. Thirdly, the expression of epithelial marker E-cadherin, proved to interact with LDHA, was obviously increased in LDHA-silencing cancer cells. The recruitment of TAMs and the secretion of CCL2 were dramatically reduced after LDHA was knocked down in vitro and in vivo. Taken together, LDHA mediates a vicious cycle of mutual promotion between BCSCs plasticity and TAMs infiltration, which may provide an effective treatment strategy by targeting LDHA for breast cancer patients.

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Section: Discussionmentioning

confidence: 99%

Lactate Dehydrogenase-A (LDH-A) Preserves Cancer Stemness and Recruitment of Tumor-Associated Macrophages to Promote Breast Cancer Progression

Wang

et al. 2021

Front. Oncol.

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“…Compared with the previous two imaging methods, breast MRI is the most sensitive and accurate imaging method for preoperative staging of breast cancer, and its sensitivity for tumor diagnosis is not affected by the density [ 11 , 67 ]. MRI provides information indicating tumor biological function: spectral imaging can determine the function of chemical components within tissue regions for qualitative tumor diagnosis through metabolite detection [ 68 ]; or MRI spectral imaging can be used for quantitative assessment of lipid composition in the breast based on altered adipogenesis genes in breast cancers with vascular infiltration, allowing prediction of tumor vascular infiltration events [ 69 ], assessment of chemotherapy response [ 70 ], and subtype identification [ 71 ]. Diffusion-weighted imaging (DWI) is highly sensitive for breast cancer as it reveals and evaluates local pathophysiological features by measuring the mobile phase of water molecules in the tissue.…”

Section: Resultsmentioning

confidence: 99%

Overview of Artificial Intelligence in Breast Cancer Medical Imaging

Zheng

Jing

2023

JCM

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The heavy global burden and mortality of breast cancer emphasize the importance of early diagnosis and treatment. Imaging detection is one of the main tools used in clinical practice for screening, diagnosis, and treatment efficacy evaluation, and can visualize changes in tumor size and texture before and after treatment. The overwhelming number of images, which lead to a heavy workload for radiologists and a sluggish reporting period, suggests the need for computer-aid detection techniques and platform. In addition, complex and changeable image features, heterogeneous quality of images, and inconsistent interpretation by different radiologists and medical institutions constitute the primary difficulties in breast cancer screening and imaging diagnosis. The advancement of imaging-based artificial intelligence (AI)-assisted tumor diagnosis is an ideal strategy for improving imaging diagnosis efficient and accuracy. By learning from image data input and constructing algorithm models, AI is able to recognize, segment, and diagnose tumor lesion automatically, showing promising application prospects. Furthermore, the rapid advancement of “omics” promotes a deeper and more comprehensive recognition of the nature of cancer. The fascinating relationship between tumor image and molecular characteristics has attracted attention to the radiomic and radiogenomics, which allow us to perform analysis and detection on the molecular level with no need for invasive operations. In this review, we integrate the current developments in AI-assisted imaging diagnosis and discuss the advances of AI-based breast cancer precise diagnosis from a clinical point of view. Although AI-assisted imaging breast cancer screening and detection is an emerging field and draws much attention, the clinical application of AI in tumor lesion recognition, segmentation, and diagnosis is still limited to research or in limited patients’ cohort. Randomized clinical trials based on large and high-quality cohort are lacking. This review aims to describe the progress of the imaging-based AI application in breast cancer screening and diagnosis for clinicians.

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“…Similarly, the study has found that LVI is an independent predictor of survival in breast cancer after neoadjuvant chemotherapy, some scholars also have found that the occurrence of LVI is significantly related to luminal B with HER2(-), and the basal-like subtype [ 20 ]. Cheung SM and Bo Bae Choi et al considered that preoperative magnetic resonance imaging (MRI) and diffusion-weighted imaging (DWI) can help diagnose the existence of LVI in breast cancer [ 21 , 22 ]. Actually, in our multivariate analysis about HR+ population, patients with LVI had the strongest independent association with residual positive axillary LNs (OR, 6.438; P < 0.05).…”

Section: Discussionmentioning

confidence: 99%

Predicting Axillary Response in Hormone Receptor-Positive Breast Cancer after Neoadjuvant Chemotherapy Using Real-World Data

Qiu

Zheng

et al. 2022

Journal of Oncology

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Purpose. To develop a scoring system for hormone receptor-positive (HR+) breast cancer patients who are expected to achieve axillary pathological complete response (pCR) after neoadjuvant chemotherapy (NAC). To confirm the correlation between axillary status and survival rate in HR+ breast cancer after NAC. Methods. Women from the Shanghai Jiao Tong University Breast Cancer Database (SJTU-BCDB) who underwent NAC for cT1-4N1-3M0 primary HR+ breast cancer between 2009 and 2018 were included in the study. In this case, patient follow up was performed until 2022 for those with complete data before and after NAC. The main outcome measures were the axillary pCR rate, overall survival (OS), and disease-free survival (DFS). The patients were randomly assigned to a test set (n = 175) and a validation set (n = 68) in a 7 : 3 ratio. A prediction risk score was then developed based on the odds ratios from the multivariate analysis of the test set (n = 175) before being validated in the validation set (n = 68). Finally, the Kaplan–Meier curves were used to explore the survival on this score system. Results. From the database, 243 women were included, and the median follow-up period was 47.5 months (95% confidence interval: 41.9–53.1). The axillary pCR rate was 18.9% (46 of 243), with the independent predictors of residual positive axillary lymph nodes (LNs) being lymphovascular invasion (LVI), breast conserving surgery (BCS), Ki67 < 14%, HER2 negativity, positive lymph nodes in ultrasound (US) before surgery, and stage III histological grade (All, P < 0.05 ). Using the above predictors of the model, the receiver operating characteristic (ROC) curve was used for calibration and inspection, with values for the test and validation sets being 0.847 ( P < 0.001 ; 95% CI: 0.769, 0.925) and 0.813 ( P < 0.001 ; 95% CI: 0.741, 0.885), respectively. The total risk score ranged from 0 to 6 for the multivariate analysis, and from this range, a risk score of 0–2 was defined as a low-risk group, while scores of 3–6 were defined as the high-risk one. By constructing the survival curve, it was found that the 5-year OS rates for the low-risk and high-risk groups were 89.0% and 84.2% (P = 0.236). Similarly, the 5-year DFS rates for the low-risk and high-risk groups were 80% and 68.5% (P = 0.048), respectively. In addition, axillary pathological stages were significantly correlated with the overall survival (OS) and disease-free survival (DFS) (All, P < 0.05 ). Conclusion. The prediction model showed good performance for HR + breast cancer. LVI, BCS, low Ki-67, HER2 negativity, suspected positive LNs before surgery, and stage III histological grade were all risk factors for residual positive axillary LNs. However, unlike pathological stages, achieving pCR in the axillary LNs does not affect the survival status.

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Intra-tumoural lipid composition and lymphovascular invasion in breast cancer via non-invasive magnetic resonance spectroscopy

Cited by 21 publications

References 43 publications

Lactate Dehydrogenase-A (LDH-A) Preserves Cancer Stemness and Recruitment of Tumor-Associated Macrophages to Promote Breast Cancer Progression

Lactate Dehydrogenase-A (LDH-A) Preserves Cancer Stemness and Recruitment of Tumor-Associated Macrophages to Promote Breast Cancer Progression

Overview of Artificial Intelligence in Breast Cancer Medical Imaging

Predicting Axillary Response in Hormone Receptor-Positive Breast Cancer after Neoadjuvant Chemotherapy Using Real-World Data

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